The Nottawasaga Valley Conservation Authority (NVCA) and The Oak Ridges Moraine Groundwater Program (ORMGP) have partnered to explore the applicability of the ORMGP’s historical climate data service in supporting event-based HEC-HMS models built in Southern Ontario to investigate the rainfall-runoff response to extreme summer rainfall events. As a proof of concept, the ~246km² Upper Mad River watershed was identified as a good first candidate.
Upper Mad River watershed
The HEC-HMS model code and its construction proceeded in a manor to accommodate future continuous simulation as planed by the NVCA. As such, the NVCA requested a “Deficit and Constant” method suitable for long term continuous modelling be included with the delivered model. The HEC-HMS model offered by the US Army Corps of Engineers Hydrologic Engineering Center includes such functionality as do many other model codes (PRMS, Raven, MikeSHE, HydroGeoSphere, etc.), yet it was ultimately chosen due to the code:
Snapshot of the Mad
River HEC-HMS project
The model construction phase proceeded with certain constraints such that the model can be readily simulate continuous processes. For instance, the model was built with:
It’s important to note that in practice, models are developed to be either event-based (e.g., individual extreme rainfall events) vs. continuous (e.g., long-term/seasonal hydrology, climate change, etc.) but rarely both. The ORMGP have maintains a near-real-time daily data set complete since 1901 built for long term continuous modelling needed for groundwater resource management. However, we also maintain a 6-hourly near-real-time climate data set since 2002. Both of these products are complete and are spatially distributed to thousands of ~10km² sub-watersheds covering our jurisdiction.
The following snapshot has been prepared to assist the NVCA with preparation of HEC-HMS Technical Memo (Task 1.4) describing the methods used to compile necessary data, build the model, calibrate/verify the model and conduct a sensitivity analysis.
The target for the Data Collection (Task 1.1) piece was the for the implementation of the ORMGP climate data service. As each of the HEC-HMS subbasin mapped well to the ORMGP’s sub-watershed delineation, rainfall data was nonetheless derived from the ~10km² CaPA-RDPA grid shown below. Compared with meteorological stations, the CaPA-RDPA product offers a refined spatial distribution of precipitation amounts. Given that most extreme summer events are of the convective type, many of these storms are themselves small scale and are susceptible of being unobserved by the relatively coarse station network.
HEC-HMS subasins vs CaPA-RDPA
resolution vs Nearest Active hourly climate stations
Analyze meteorological data (precipitation, snow, temperature, radiation)
Locally, there exists 3 active meteorological stations having hourly precipitation data (click to view data):
Annual precipitation in the region have seen mixed trends as of late. For instance Collingwood shows a increasing trend of annual precipitation volumes over the past 30 years, whereas a decreasing trend is found at Egbert CS and no trend at Barri-Oro.
mean daily
temperature: 8°C
Analyze existing streamflow data (characterize large events (hydrograph analysis), baseflow analysis, statistical analysis)
Instantaneous (5min) streamflow data have been acquired from 2011 for 02ED015: MAD RIVER BELOW AVENING.
(From the daily historic records), it is evident that there is a change in flow regime occurring sometime in 2005, where annual runoff yeilds show a definite increase.
cumulative discharge of both total flow and separated baseflow
A comparison of timescales was performed to identify the model time step.
Analyze applicable digital geospatial data sets including but not limited to soils, topography, land use to define hydrologic response units and appropriate catchments for the hydrologic model.
OMRF (2019b): 10m horizontal resolution.
Combination of SOLRIS v.3.0 for land use type (OMNR, 2019a) and OGS (2010) to classify the Curve Number (CN) method “hydrologic soil group”.
SOLRIS is provided as a set of land use identifiers. From these a look-up table is used to assign a data-based model parameter.
OGS 8 a set of “relative permebilities”
based on SOLRIS
relative vegetaiton cover based
on SOLRIS
Process soil characteristics
Using the “PERMEABILI” attribute of OGS (2010) soil characteristics needed to estimate infiltration loss parameters for the Upper Mad River Watershed were determined.
relative infiltration rates based
on OGS, 2010
SOLRIS land use types and OGS’s
based on a geospatial overlay of
SOLRIS and OGS
Upper Mad River Hydrologic Modeling Using HEC-HMS (Task 1.2)
Delineate climate zones and subbasins and will complete meteorological and streamflow data processing for the Upper Mad River watershed.
kable(df, caption = 'cell-border stripe')
| ï..name | percov | CN | perimp | metID | flowlen.km | fpslp | basinslp | bsnrelief | bsnrelrati | elongation | drndens | area | swsid | dssws |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subbasin-2 | 0.0927624 | 69.70073 | 0.0678313 | 12495453 | 6.95290 | 0.02519 | 0.04202 | 175.5799 | 0.02525 | 0.54685 | 1.02080 | 11.348858 | 0 | -1 |
| Subbasin-3 | 0.1272083 | 83.72658 | 0.0242185 | 12940644 | 9.76037 | 0.02263 | 0.07809 | 226.3828 | 0.02319 | 0.37984 | 0.99890 | 10.788892 | 1 | -1 |
| Subbasin-1 | 0.3479206 | 71.40088 | 0.0234130 | 12495453 | 4.01904 | 0.04948 | 0.11811 | 216.1292 | 0.05378 | 0.73244 | 0.72994 | 6.802537 | 2 | 3 |
| Subbasin-5 | 0.2616093 | 72.37493 | 0.0676880 | 12660504 | 7.30687 | 0.03337 | 0.08361 | 246.0634 | 0.03368 | 0.46110 | 1.24863 | 8.910771 | 3 | 1 |
| Subbasin-4 | 0.1278012 | 75.87059 | 0.0159263 | 12495453 | 5.85970 | 0.01837 | 0.07874 | 697.1606 | 0.11898 | 0.54728 | 0.50650 | 8.074160 | 4 | 10 |
| Subbasin-7 | 0.3371822 | 77.11174 | 0.0426831 | 12350375 | 15.14501 | 0.01627 | 0.10490 | 247.0075 | 0.01631 | 0.32158 | 0.93037 | 18.622390 | 5 | 10 |
| Subbasin-6 | 0.3461948 | 79.19955 | 0.0105703 | 12350375 | 6.65202 | 0.00519 | 0.07161 | 60.7784 | 0.00914 | 0.61148 | 0.79997 | 12.989416 | 6 | 5 |
| Subbasin-20 | 0.3411663 | 69.15127 | 0.0071032 | 13125281 | 8.71339 | 0.00132 | 0.01939 | 16.0768 | 0.00185 | 0.47365 | 0.88549 | 13.368437 | 7 | 23 |
| Subbasin-11 | 0.2909395 | 71.96663 | 0.0154423 | 12725234 | 11.09162 | 0.00134 | 0.02081 | 21.8456 | 0.00197 | 0.31928 | 1.09702 | 9.843678 | 8 | 23 |
| Subbasin-15 | 0.2955138 | 83.20826 | 0.0224271 | 12350375 | 5.84727 | 0.01637 | 0.06147 | 107.1488 | 0.01832 | 0.62234 | 0.76277 | 10.395453 | 9 | 13 |
| Subbasin-9 | 0.3349957 | 72.54164 | 0.0175558 | 12495453 | 3.88383 | 0.03198 | 0.13170 | 127.6565 | 0.03287 | 0.58502 | 0.81165 | 4.052843 | 10 | 2 |
| Subbasin-19 | 0.2876643 | 72.97874 | 0.0131664 | 12525261 | 6.85894 | 0.00338 | 0.03846 | 32.1715 | 0.00469 | 0.52718 | 0.94596 | 10.263243 | 11 | 24 |
| Subbasin-10 | 0.4950839 | 56.72039 | 0.0222907 | 12495453 | 6.00332 | 0.03303 | 0.13482 | 204.7157 | 0.03410 | 0.59790 | 0.88920 | 10.113434 | 12 | 2 |
| Subbasin-24 | 0.3042713 | 70.97325 | 0.0119758 | 12495453 | 8.43930 | 0.02363 | 0.11278 | 229.0075 | 0.02714 | 0.37488 | 0.92397 | 7.857712 | 13 | 10 |
| Subbasin-18 | 0.3269311 | 60.91327 | 0.0223886 | 12495453 | 6.94438 | 0.02300 | 0.11068 | 176.4274 | 0.02541 | 0.55750 | 1.15755 | 11.765281 | 14 | 12 |
| Subbasin-16 | 0.3836976 | 70.44204 | 0.0172621 | 12495453 | 3.86697 | 0.04182 | 0.11398 | 168.3357 | 0.04353 | 0.65048 | 0.69902 | 4.966481 | 15 | 14 |
| Subbasin-25 | 0.4509512 | 76.17287 | 0.0223504 | 13045410 | 5.24715 | 0.02839 | 0.13271 | 152.3476 | 0.02903 | 0.54670 | 0.74785 | 6.459028 | 16 | 15 |
| Subbasin-17 | 0.2841102 | 83.16054 | 0.0199571 | 12495453 | 10.03688 | 0.01690 | 0.06412 | 170.8054 | 0.01702 | 0.39144 | 0.95163 | 12.116451 | 17 | 15 |
| Subbasin-22 | 0.1356595 | 87.00998 | 0.0170055 | 13285364 | 5.96290 | 0.00315 | 0.02824 | 22.9576 | 0.00385 | 0.52754 | 0.73365 | 7.765971 | 18 | 21 |
| Subbasin-27 | 0.1760634 | 75.54790 | 0.0243670 | 13045410 | 5.91650 | 0.00647 | 0.04914 | 53.7912 | 0.00909 | 0.59947 | 1.06257 | 9.873535 | 19 | 16 |
| Subbasin-14 | 0.1534925 | 86.45944 | 0.0203897 | 13510322 | 8.28297 | 0.00447 | 0.02245 | 38.0291 | 0.00459 | 0.42898 | 0.81870 | 9.908591 | 20 | 21 |
| Subbasin-29 | 0.2114516 | 86.51667 | 0.0212226 | 13125281 | 5.88207 | 0.00541 | 0.03001 | 39.7751 | 0.00676 | 0.41407 | 1.30505 | 4.655897 | 21 | 19 |
| Subbasin-12 | 0.2051869 | 77.89967 | 0.0254132 | 13125281 | 10.83541 | 0.00353 | 0.03326 | 51.2772 | 0.00473 | 0.44367 | 0.99767 | 18.137188 | 22 | 21 |
| Subbasin-26 | 0.1337561 | 81.87401 | 0.0197080 | 12945267 | 7.81829 | 0.00303 | 0.03407 | 25.3484 | 0.00324 | 0.55457 | 0.97765 | 14.755358 | 23 | 11 |
| Subbasin-13 | 0.1496254 | 81.80116 | 0.0171066 | 12435285 | 11.87524 | 0.00399 | 0.03623 | 48.1672 | 0.00406 | 0.37043 | 0.94896 | 15.190600 | 24 | 6 |
| Subbasin-8 | 0.3410768 | 77.26844 | 0.0191042 | 12350375 | 8.06255 | 0.00367 | 0.05344 | 42.4820 | 0.00527 | 0.43617 | 0.77690 | 9.708267 | 25 | 6 |
The HEC-HMS model of the Mad river consists of 27 subbasins, 25 of which drain to the sole hydrometric station at Avening. The HEC-HMS model was designed for event-based analysis using the SCS-CN methodology for runoff production, a Syder unit hydrograph for basin transfer, a simple lag function for reach transfer and a simple recession coefficient baseflow simulator activated by a ratio to simulated peak (USACE, 2000).
The (sub-)model used in the HEC-HMS design include: - Loss method: Soil Conservation Service (SCS) curve number - Transform method: Snyder unit hydrograph - Routing method: simple lag - Routing method: simple recession
The “free” parameters are applied uniformly (i.e., globally) over the model. Differences in the water budgeting at each subbasin would then attributed to: 1. land use mapping 1. surficial geology mapping 1. topography (DEM), defining: - subbasin shape and - reach length.
A total of 12 annual extreme events were selected
The HEC-HMS hydrologic model was calibrated and verified using available streamflow gauge data (Task 1.3). A range of annual extreme events exceeding the 2-yr return period are used to simulate the complete flow regime.
Minimize the peak-weighted root mean square error objective function (USACE, 1998)
\[ Z = \sqrt{\frac{1}{n}\sum^n\left[ \left(q_s-q_o\right)^2\cdot\left(\frac{q_o-\overline{q_o}}{\overline{q_o}}\right)\right]} \]
All 7 parameters (\(t_p\), \(c_p\), \(k\), \(r_p\), \(lag\), \(f_{ia}\), and \(f_{CN}\)) were fed into a Shuffled Complex Evolution (SCE - Duan et.al., 1993) optimization scheme. All events were optimized in this trial to assess two things: 1. inter-dependencies among model parameters. If the selection of a parameter can be confidently estimate by another (or an initial condition), then the dimensionality of the inverse problem is reduced 1. parameter identifiability: Are there optimized parameters that appear to seek a particular value?
trial 1 correlation
matrix
the SCS Curve Number method for the and the Timmins storm as per NVCA guidelines
Three forms synthetic hyetographs were developed to test design events now and under the changing climate. The Timmins Storm is pre-defined while the SCS design storms and the climate change projections are constructed using the “alternating block” synthetic hyetographs (NRC-PCS, 2018).
The 2, 5, 10, 25, 50, 100-year SCS II design storms were re-casted as synthetic hyetographs using the alternate block method.
The Timmins Storm was a local (Ontario) event/disaster on August 31, 1961. It’s hyetograph is given as (mm):
Total: 193 mm storm
alternating block IDF-CC tool (Simonovic et.al., 2015)
In total this comes to \(3\times 3 \times 1=9\)
IDFs are defined by (Simonovic et.al., 2015):
\[ i=A\cdot \left(t+t_0\right)^B \]
where \(i\) is rainfall rate (mm/hr), \(t\) duration of precipitation event (hr), \(A\), \(B\) and \(t_0\) are coefficients provided by the IDF-CC tool.
T (years) | Coefficient \(A\) | Coefficient \(B\) | Coefficient \(t_0\) 2 22.1 -0.755 0.070 5 30.1 -0.771 0.091 10 35.6 -0.780 0.103 20 40.9 -0.788 0.112 25 42.6 -0.790 0.115 50 47.8 -0.796 0.123 100 53.0 -0.802 0.129
Duan, Q.Y., V.K. Gupta, and S. Sorooshian, 1993. Shuffled Complex Evolution Approach for Effective and Efficient Global Minimization. Journal of Optimization Theory and Applications 76(3) pp.501-521.
Natural Resources Canada, Public Safety Canada. 2018. Case studies on climate change in floodplain mapping v.1 ANNEX C: FLOOD MAPPING AND CLIMATE CHANGE: WATERFORD RIVER CASE STUDY ANALYSIS.
Ontario Geological Survey 2010. Surficial geology of southern Ontario; Ontario Geological Survey, Miscellaneous Release— Data 128 – Revised.
Ontario Ministry of Natural Resources and Forestry, 2019a. Southern Ontario Land Resource Information System (SOLRIS) Version 3.0: Data Specifications. Science and Research Branch, April 2019
Ontario Ministry of Natural Resources and Forestry, 2019b. Ontario Digital Elevation Model (Imagery-Derived).
Simonovic, S.P., A. Schardong, R. Srivastav, and D. Sandink (2015), IDF_CC Web-based Tool for Updating Intensity-Duration-Frequency Curves to Changing Climate – ver 6.5, Western University Facility for Intelligent Decision Support and Institute for Catastrophic Loss Reduction, open access https://www.idf-cc-uwo.ca.
US Army Corps of Engineers, USACE (1998). HEC-1 flood hydrograph package user’s manual. Hydrologic Engineering Center, Davis, CA.
US Army Corps of Engineers, USACE (2000). Hydrologic Modeling System HEC-HMS Technical Reference Manual. Hydrologic Engineering Center, Davis, CA.